In real application scenarios, in addition to processing real images, it is also important to handle arbitrary scale factors with a single model. To achieve this, Hu et al. proposed two simple but powerful methods termed Meta-SR [1]} and Meta-USR [2]}. Among them, Meta-SR is the first SISR method that can be used for arbitrary scale factors and Meta-USR is an improved version that can be applied to arbitrary degradation mode (including arbitrary scale factors). Although Meta-SR and Meta-USR achieve promising performance on non-integer scale factors, they cannot handle SR with asymmetric scale factors. To alleviate this problem, Wang et al. [3]} suggested learning the scale-arbitrary SISR model from scale-specific networks and developed a plug-in module for existing models to achieve scale-arbitrary SR. Specifically, the proposed plug-in module uses conditional convolution to dynamically generate filters based on the input scale information, thus the networks equipped with the proposed module achieve promising results for arbitrary scales with only a single model.
